A new influence maximization algorithm based on GC-centrality

Wei Liu; Qi Shen; Chengzhe Li

doi:10.1117/12.3049605

8 November 2024 A new influence maximization algorithm based on GC-centrality

Wei Liu, Qi Shen, Chengzhe Li

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134160P (2024) https://doi.org/10.1117/12.3049605
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

The Influence Maximization (IM) problem aims to find a set of seed nodes to maximize the spread of their influence in social networks. This issue has a very important application background in viral marketing and has attracted extensive research in academia and industry. In order to find nodes with more widely influence and core position in the entire network, this paper proposes a new centrality ranking strategy named Influence Maximization Algorithm based on GC-Centrality. Firstly, GC-Centrality is defined to search for the core links of the network, and then the proposed indicators are used to select seed nodes while suppressing their neighbours. Finally, two experiments were conducted on real datasets, indicating that the proposed algorithm is superior to the comparison algorithm.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Liu, Qi Shen, and Chengzhe Li "A new influence maximization algorithm based on GC-centrality", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134160P (8 November 2024); https://doi.org/10.1117/12.3049605

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